Overview

Dataset statistics

Number of variables16
Number of observations1311
Missing cells588
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory206.4 KiB
Average record size in memory161.2 B

Variable types

Categorical4
Numeric12

Alerts

owner has a high cardinality: 305 distinct valuesHigh cardinality
aroma is highly overall correlated with flavor and 6 other fieldsHigh correlation
flavor is highly overall correlated with aroma and 6 other fieldsHigh correlation
aftertaste is highly overall correlated with aroma and 6 other fieldsHigh correlation
acidity is highly overall correlated with aroma and 6 other fieldsHigh correlation
body is highly overall correlated with aroma and 6 other fieldsHigh correlation
balance is highly overall correlated with aroma and 6 other fieldsHigh correlation
uniformity is highly overall correlated with clean_cupHigh correlation
clean_cup is highly overall correlated with uniformityHigh correlation
cupper_points is highly overall correlated with aroma and 6 other fieldsHigh correlation
total_cup_points is highly overall correlated with aroma and 6 other fieldsHigh correlation
variety has 201 (15.3%) missing valuesMissing
processing_method has 152 (11.6%) missing valuesMissing
altitude_mean_meters has 227 (17.3%) missing valuesMissing
altitude_mean_meters is highly skewed (γ1 = 20.09518078)Skewed

Reproduction

Analysis started2023-07-28 17:56:35.248364
Analysis finished2023-07-28 17:57:02.646902
Duration27.4 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

owner
Categorical

Distinct305
Distinct (%)23.4%
Missing7
Missing (%)0.5%
Memory size20.5 KiB
juan luis alvarado romero
155 
racafe & cia s.c.a
 
60
exportadora de cafe condor s.a
 
54
kona pacific farmers cooperative
 
52
ipanema coffees
 
50
Other values (300)
933 

Length

Max length50
Median length40
Mean length21.347393
Min length3

Characters and Unicode

Total characters27837
Distinct characters51
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167 ?
Unique (%)12.8%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowgrounds for health admin
4th rowyidnekachew dabessa
5th rowmetad plc

Common Values

ValueCountFrequency (%)
juan luis alvarado romero 155
 
11.8%
racafe & cia s.c.a 60
 
4.6%
exportadora de cafe condor s.a 54
 
4.1%
kona pacific farmers cooperative 52
 
4.0%
ipanema coffees 50
 
3.8%
cqi taiwan icp cqi台灣合作夥伴 47
 
3.6%
lin, che-hao krude 林哲豪 30
 
2.3%
nucoffee 29
 
2.2%
carcafe ltda ci 27
 
2.1%
the coffee source inc. 23
 
1.8%
Other values (295) 777
59.3%

Length

2023-07-28T19:57:02.778896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
luis 167
 
3.9%
juan 160
 
3.8%
alvarado 155
 
3.6%
romero 155
 
3.6%
de 114
 
2.7%
s.a 101
 
2.4%
coffee 83
 
1.9%
cafe 72
 
1.7%
exportadora 70
 
1.6%
coffees 67
 
1.6%
Other values (642) 3115
73.1%

Most occurring characters

ValueCountFrequency (%)
a 3397
12.2%
2963
 
10.6%
e 2534
 
9.1%
o 2173
 
7.8%
r 1962
 
7.0%
i 1619
 
5.8%
c 1614
 
5.8%
n 1326
 
4.8%
l 1127
 
4.0%
s 1087
 
3.9%
Other values (41) 8035
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23889
85.8%
Space Separator 2963
 
10.6%
Other Punctuation 512
 
1.8%
Other Letter 404
 
1.5%
Dash Punctuation 43
 
0.2%
Open Punctuation 13
 
< 0.1%
Close Punctuation 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3397
14.2%
e 2534
10.6%
o 2173
 
9.1%
r 1962
 
8.2%
i 1619
 
6.8%
c 1614
 
6.8%
n 1326
 
5.6%
l 1127
 
4.7%
s 1087
 
4.6%
d 945
 
4.0%
Other values (21) 6105
25.6%
Other Letter
ValueCountFrequency (%)
47
11.6%
47
11.6%
47
11.6%
47
11.6%
47
11.6%
47
11.6%
30
7.4%
30
7.4%
30
7.4%
8
 
2.0%
Other values (3) 24
5.9%
Other Punctuation
ValueCountFrequency (%)
. 377
73.6%
, 72
 
14.1%
& 63
 
12.3%
Space Separator
ValueCountFrequency (%)
2963
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23889
85.8%
Common 3544
 
12.7%
Han 404
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3397
14.2%
e 2534
10.6%
o 2173
 
9.1%
r 1962
 
8.2%
i 1619
 
6.8%
c 1614
 
6.8%
n 1326
 
5.6%
l 1127
 
4.7%
s 1087
 
4.6%
d 945
 
4.0%
Other values (21) 6105
25.6%
Han
ValueCountFrequency (%)
47
11.6%
47
11.6%
47
11.6%
47
11.6%
47
11.6%
47
11.6%
30
7.4%
30
7.4%
30
7.4%
8
 
2.0%
Other values (3) 24
5.9%
Common
ValueCountFrequency (%)
2963
83.6%
. 377
 
10.6%
, 72
 
2.0%
& 63
 
1.8%
- 43
 
1.2%
( 13
 
0.4%
) 13
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27392
98.4%
CJK 404
 
1.5%
None 41
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3397
12.4%
2963
 
10.8%
e 2534
 
9.3%
o 2173
 
7.9%
r 1962
 
7.2%
i 1619
 
5.9%
c 1614
 
5.9%
n 1326
 
4.8%
l 1127
 
4.1%
s 1087
 
4.0%
Other values (23) 7590
27.7%
CJK
ValueCountFrequency (%)
47
11.6%
47
11.6%
47
11.6%
47
11.6%
47
11.6%
47
11.6%
30
7.4%
30
7.4%
30
7.4%
8
 
2.0%
Other values (3) 24
5.9%
None
ValueCountFrequency (%)
ñ 23
56.1%
é 12
29.3%
á 3
 
7.3%
ú 2
 
4.9%
ó 1
 
2.4%
Distinct36
Distinct (%)2.7%
Missing1
Missing (%)0.1%
Memory size20.5 KiB
Mexico
236 
Colombia
183 
Guatemala
181 
Brazil
132 
Taiwan
75 
Other values (31)
503 

Length

Max length28
Median length27
Mean length8.8931298
Min length4

Characters and Unicode

Total characters11650
Distinct characters47
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.6%

Sample

1st rowEthiopia
2nd rowEthiopia
3rd rowGuatemala
4th rowEthiopia
5th rowEthiopia

Common Values

ValueCountFrequency (%)
Mexico 236
18.0%
Colombia 183
14.0%
Guatemala 181
13.8%
Brazil 132
10.1%
Taiwan 75
 
5.7%
United States (Hawaii) 73
 
5.6%
Honduras 53
 
4.0%
Costa Rica 51
 
3.9%
Ethiopia 44
 
3.4%
Tanzania, United Republic Of 40
 
3.1%
Other values (26) 242
18.5%

Length

2023-07-28T19:57:02.888635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mexico 236
14.1%
colombia 183
 
11.0%
guatemala 181
 
10.8%
brazil 132
 
7.9%
united 125
 
7.5%
states 85
 
5.1%
taiwan 75
 
4.5%
hawaii 73
 
4.4%
honduras 53
 
3.2%
costa 51
 
3.1%
Other values (35) 477
28.5%

Most occurring characters

ValueCountFrequency (%)
a 1933
16.6%
i 1267
 
10.9%
o 805
 
6.9%
e 742
 
6.4%
l 626
 
5.4%
t 590
 
5.1%
n 502
 
4.3%
m 384
 
3.3%
361
 
3.1%
c 358
 
3.1%
Other values (37) 4082
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9423
80.9%
Uppercase Letter 1671
 
14.3%
Space Separator 361
 
3.1%
Open Punctuation 77
 
0.7%
Close Punctuation 77
 
0.7%
Other Punctuation 41
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1933
20.5%
i 1267
13.4%
o 805
8.5%
e 742
 
7.9%
l 626
 
6.6%
t 590
 
6.3%
n 502
 
5.3%
m 384
 
4.1%
c 358
 
3.8%
u 323
 
3.4%
Other values (13) 1893
20.1%
Uppercase Letter
ValueCountFrequency (%)
M 256
15.3%
C 251
15.0%
G 182
10.9%
U 151
9.0%
T 147
8.8%
B 134
8.0%
H 132
7.9%
S 106
6.3%
R 96
 
5.7%
E 66
 
3.9%
Other values (9) 150
9.0%
Other Punctuation
ValueCountFrequency (%)
, 40
97.6%
? 1
 
2.4%
Space Separator
ValueCountFrequency (%)
361
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11094
95.2%
Common 556
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1933
17.4%
i 1267
 
11.4%
o 805
 
7.3%
e 742
 
6.7%
l 626
 
5.6%
t 590
 
5.3%
n 502
 
4.5%
m 384
 
3.5%
c 358
 
3.2%
u 323
 
2.9%
Other values (32) 3564
32.1%
Common
ValueCountFrequency (%)
361
64.9%
( 77
 
13.8%
) 77
 
13.8%
, 40
 
7.2%
? 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1933
16.6%
i 1267
 
10.9%
o 805
 
6.9%
e 742
 
6.4%
l 626
 
5.4%
t 590
 
5.1%
n 502
 
4.3%
m 384
 
3.3%
361
 
3.1%
c 358
 
3.1%
Other values (37) 4082
35.0%

variety
Categorical

Distinct29
Distinct (%)2.6%
Missing201
Missing (%)15.3%
Memory size20.5 KiB
Caturra
256 
Bourbon
226 
Typica
211 
Other
108 
Catuai
74 
Other values (24)
235 

Length

Max length21
Median length19
Mean length7.0216216
Min length4

Characters and Unicode

Total characters7794
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.5%

Sample

1st rowOther
2nd rowBourbon
3rd rowOther
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
Caturra 256
19.5%
Bourbon 226
17.2%
Typica 211
16.1%
Other 108
8.2%
Catuai 74
 
5.6%
Hawaiian Kona 44
 
3.4%
Yellow Bourbon 35
 
2.7%
Mundo Novo 33
 
2.5%
Catimor 20
 
1.5%
SL14 17
 
1.3%
Other values (19) 86
 
6.6%
(Missing) 201
15.3%

Length

2023-07-28T19:57:02.998010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bourbon 261
21.2%
caturra 256
20.8%
typica 211
17.1%
other 108
8.8%
catuai 74
 
6.0%
hawaiian 44
 
3.6%
kona 44
 
3.6%
yellow 35
 
2.8%
mundo 33
 
2.7%
novo 33
 
2.7%
Other values (25) 133
10.8%

Most occurring characters

ValueCountFrequency (%)
a 1174
15.1%
r 936
12.0%
o 731
 
9.4%
u 643
 
8.2%
t 468
 
6.0%
i 413
 
5.3%
n 398
 
5.1%
C 351
 
4.5%
b 267
 
3.4%
B 263
 
3.4%
Other values (36) 2150
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6318
81.1%
Uppercase Letter 1270
 
16.3%
Space Separator 122
 
1.6%
Decimal Number 84
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1174
18.6%
r 936
14.8%
o 731
11.6%
u 643
10.2%
t 468
 
7.4%
i 413
 
6.5%
n 398
 
6.3%
b 267
 
4.2%
c 235
 
3.7%
y 217
 
3.4%
Other values (13) 836
13.2%
Uppercase Letter
ValueCountFrequency (%)
C 351
27.6%
B 263
20.7%
T 211
16.6%
O 108
 
8.5%
S 45
 
3.5%
H 45
 
3.5%
K 44
 
3.5%
L 41
 
3.2%
M 40
 
3.1%
Y 37
 
2.9%
Other values (7) 85
 
6.7%
Decimal Number
ValueCountFrequency (%)
4 25
29.8%
1 21
25.0%
2 15
17.9%
8 15
17.9%
3 8
 
9.5%
Space Separator
ValueCountFrequency (%)
122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7588
97.4%
Common 206
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1174
15.5%
r 936
12.3%
o 731
 
9.6%
u 643
 
8.5%
t 468
 
6.2%
i 413
 
5.4%
n 398
 
5.2%
C 351
 
4.6%
b 267
 
3.5%
B 263
 
3.5%
Other values (30) 1944
25.6%
Common
ValueCountFrequency (%)
122
59.2%
4 25
 
12.1%
1 21
 
10.2%
2 15
 
7.3%
8 15
 
7.3%
3 8
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7794
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1174
15.1%
r 936
12.0%
o 731
 
9.4%
u 643
 
8.2%
t 468
 
6.0%
i 413
 
5.3%
n 398
 
5.1%
C 351
 
4.5%
b 267
 
3.4%
B 263
 
3.4%
Other values (36) 2150
27.6%
Distinct5
Distinct (%)0.4%
Missing152
Missing (%)11.6%
Memory size20.5 KiB
Washed / Wet
812 
Natural / Dry
251 
Semi-washed / Semi-pulped
 
56
Other
 
26
Pulped natural / honey
 
14

Length

Max length25
Median length12
Mean length12.808456
Min length5

Characters and Unicode

Total characters14845
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWashed / Wet
2nd rowWashed / Wet
3rd rowNatural / Dry
4th rowWashed / Wet
5th rowNatural / Dry

Common Values

ValueCountFrequency (%)
Washed / Wet 812
61.9%
Natural / Dry 251
 
19.1%
Semi-washed / Semi-pulped 56
 
4.3%
Other 26
 
2.0%
Pulped natural / honey 14
 
1.1%
(Missing) 152
 
11.6%

Length

2023-07-28T19:57:03.123008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-28T19:57:03.263650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1133
32.9%
washed 812
23.6%
wet 812
23.6%
natural 265
 
7.7%
dry 251
 
7.3%
semi-washed 56
 
1.6%
semi-pulped 56
 
1.6%
other 26
 
0.8%
pulped 14
 
0.4%
honey 14
 
0.4%

Most occurring characters

ValueCountFrequency (%)
2280
15.4%
e 1902
12.8%
W 1624
10.9%
a 1398
9.4%
/ 1133
7.6%
t 1103
7.4%
d 938
6.3%
h 908
 
6.1%
s 868
 
5.8%
r 542
 
3.7%
Other values (15) 2149
14.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9042
60.9%
Space Separator 2280
 
15.4%
Uppercase Letter 2278
 
15.3%
Other Punctuation 1133
 
7.6%
Dash Punctuation 112
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1902
21.0%
a 1398
15.5%
t 1103
12.2%
d 938
10.4%
h 908
10.0%
s 868
9.6%
r 542
 
6.0%
l 335
 
3.7%
u 335
 
3.7%
y 265
 
2.9%
Other values (6) 448
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
W 1624
71.3%
N 251
 
11.0%
D 251
 
11.0%
S 112
 
4.9%
O 26
 
1.1%
P 14
 
0.6%
Space Separator
ValueCountFrequency (%)
2280
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1133
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11320
76.3%
Common 3525
 
23.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1902
16.8%
W 1624
14.3%
a 1398
12.3%
t 1103
9.7%
d 938
8.3%
h 908
8.0%
s 868
7.7%
r 542
 
4.8%
l 335
 
3.0%
u 335
 
3.0%
Other values (12) 1367
12.1%
Common
ValueCountFrequency (%)
2280
64.7%
/ 1133
32.1%
- 112
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14845
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2280
15.4%
e 1902
12.8%
W 1624
10.9%
a 1398
9.4%
/ 1133
7.6%
t 1103
7.4%
d 938
6.3%
h 908
 
6.1%
s 868
 
5.8%
r 542
 
3.7%
Other values (15) 2149
14.5%

aroma
Real number (ℝ)

Distinct33
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5638063
Minimum0
Maximum8.75
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-28T19:57:03.561041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.08
Q17.42
median7.58
Q37.75
95-th percentile8.08
Maximum8.75
Range8.75
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.37866631
Coefficient of variation (CV)0.050062931
Kurtosis122.3781
Mean7.5638063
Median Absolute Deviation (MAD)0.17
Skewness-6.306326
Sum9916.15
Variance0.14338817
MonotonicityNot monotonic
2023-07-28T19:57:03.670419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7.67 173
13.2%
7.5 163
12.4%
7.58 149
11.4%
7.75 122
9.3%
7.42 121
9.2%
7.83 101
7.7%
7.33 96
7.3%
7.25 78
 
5.9%
7.92 57
 
4.3%
7.17 45
 
3.4%
Other values (23) 206
15.7%
ValueCountFrequency (%)
0 1
 
0.1%
5.08 1
 
0.1%
6.17 1
 
0.1%
6.33 1
 
0.1%
6.42 1
 
0.1%
6.5 2
 
0.2%
6.67 3
 
0.2%
6.75 6
0.5%
6.83 9
0.7%
6.92 14
1.1%
ValueCountFrequency (%)
8.75 1
 
0.1%
8.67 2
 
0.2%
8.58 1
 
0.1%
8.5 3
 
0.2%
8.42 9
 
0.7%
8.33 6
 
0.5%
8.25 9
 
0.7%
8.17 20
1.5%
8.08 20
1.5%
8 43
3.3%

flavor
Real number (ℝ)

Distinct35
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5180702
Minimum0
Maximum8.83
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-28T19:57:03.795417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.92
Q17.33
median7.58
Q37.75
95-th percentile8
Maximum8.83
Range8.83
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.39997921
Coefficient of variation (CV)0.053202378
Kurtosis95.172934
Mean7.5180702
Median Absolute Deviation (MAD)0.17
Skewness-5.2235119
Sum9856.19
Variance0.15998337
MonotonicityNot monotonic
2023-07-28T19:57:03.889169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7.5 164
12.5%
7.58 162
12.4%
7.67 145
11.1%
7.75 120
9.2%
7.42 114
8.7%
7.33 110
8.4%
7.83 85
 
6.5%
7.25 64
 
4.9%
7.17 56
 
4.3%
7.08 42
 
3.2%
Other values (25) 249
19.0%
ValueCountFrequency (%)
0 1
 
0.1%
6.08 1
 
0.1%
6.17 2
 
0.2%
6.33 3
 
0.2%
6.42 1
 
0.1%
6.5 9
0.7%
6.58 5
 
0.4%
6.67 4
 
0.3%
6.75 10
0.8%
6.83 16
1.2%
ValueCountFrequency (%)
8.83 1
 
0.1%
8.67 4
 
0.3%
8.58 2
 
0.2%
8.5 5
 
0.4%
8.42 5
 
0.4%
8.33 5
 
0.4%
8.25 7
 
0.5%
8.17 18
1.4%
8.08 13
 
1.0%
8 41
3.1%

aftertaste
Real number (ℝ)

Distinct35
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3976964
Minimum0
Maximum8.67
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-28T19:57:04.014164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.83
Q17.25
median7.42
Q37.58
95-th percentile7.92
Maximum8.67
Range8.67
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.40511863
Coefficient of variation (CV)0.054762808
Kurtosis84.644948
Mean7.3976964
Median Absolute Deviation (MAD)0.17
Skewness-4.8450553
Sum9698.38
Variance0.1641211
MonotonicityNot monotonic
2023-07-28T19:57:04.123544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7.5 162
12.4%
7.33 150
11.4%
7.42 127
9.7%
7.58 125
9.5%
7.25 103
7.9%
7.67 99
 
7.6%
7.17 90
 
6.9%
7.75 81
 
6.2%
7 62
 
4.7%
7.83 61
 
4.7%
Other values (25) 251
19.1%
ValueCountFrequency (%)
0 1
 
0.1%
6.17 8
 
0.6%
6.25 1
 
0.1%
6.33 6
 
0.5%
6.42 4
 
0.3%
6.5 6
 
0.5%
6.58 6
 
0.5%
6.67 14
 
1.1%
6.75 9
 
0.7%
6.83 36
2.7%
ValueCountFrequency (%)
8.67 1
 
0.1%
8.58 2
 
0.2%
8.5 4
 
0.3%
8.42 3
 
0.2%
8.33 2
 
0.2%
8.25 4
 
0.3%
8.17 7
 
0.5%
8.08 7
 
0.5%
8 27
2.1%
7.92 19
1.4%

acidity
Real number (ℝ)

Distinct31
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5331121
Minimum0
Maximum8.75
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-28T19:57:04.233315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q17.33
median7.5
Q37.75
95-th percentile8.08
Maximum8.75
Range8.75
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.38159879
Coefficient of variation (CV)0.050656194
Kurtosis116.27208
Mean7.5331121
Median Absolute Deviation (MAD)0.17
Skewness-5.9678735
Sum9875.91
Variance0.14561764
MonotonicityNot monotonic
2023-07-28T19:57:04.342690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7.5 160
12.2%
7.58 150
11.4%
7.67 143
10.9%
7.42 127
9.7%
7.75 122
9.3%
7.33 110
8.4%
7.25 86
 
6.6%
7.17 73
 
5.6%
7.83 72
 
5.5%
8 47
 
3.6%
Other values (21) 221
16.9%
ValueCountFrequency (%)
0 1
 
0.1%
5.25 1
 
0.1%
6.08 1
 
0.1%
6.25 1
 
0.1%
6.5 1
 
0.1%
6.67 5
 
0.4%
6.75 6
 
0.5%
6.83 11
 
0.8%
6.92 10
 
0.8%
7 32
2.4%
ValueCountFrequency (%)
8.75 1
 
0.1%
8.58 1
 
0.1%
8.5 7
 
0.5%
8.42 6
 
0.5%
8.33 9
 
0.7%
8.25 6
 
0.5%
8.17 14
 
1.1%
8.08 25
1.9%
8 47
3.6%
7.92 46
3.5%

body
Real number (ℝ)

Distinct31
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5177269
Minimum0
Maximum8.58
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-28T19:57:04.452066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.08
Q17.33
median7.5
Q37.67
95-th percentile8
Maximum8.58
Range8.58
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.35921291
Coefficient of variation (CV)0.047782117
Kurtosis146.90949
Mean7.5177269
Median Absolute Deviation (MAD)0.17
Skewness-7.1554372
Sum9855.74
Variance0.12903391
MonotonicityNot monotonic
2023-07-28T19:57:04.547762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7.5 198
15.1%
7.67 149
11.4%
7.58 136
10.4%
7.33 131
10.0%
7.42 125
9.5%
7.75 108
8.2%
7.25 86
6.6%
7.83 82
6.3%
7.17 68
 
5.2%
7.92 48
 
3.7%
Other values (21) 180
13.7%
ValueCountFrequency (%)
0 1
 
0.1%
5.25 1
 
0.1%
6.33 2
 
0.2%
6.42 1
 
0.1%
6.5 1
 
0.1%
6.67 2
 
0.2%
6.75 4
 
0.3%
6.83 4
 
0.3%
6.92 11
 
0.8%
7 34
2.6%
ValueCountFrequency (%)
8.58 1
 
0.1%
8.5 3
 
0.2%
8.42 3
 
0.2%
8.33 6
 
0.5%
8.25 5
 
0.4%
8.17 7
 
0.5%
8.08 21
 
1.6%
8 34
2.6%
7.92 48
3.7%
7.83 82
6.3%

balance
Real number (ℝ)

Distinct32
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5175057
Minimum0
Maximum8.75
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-28T19:57:04.656264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.92
Q17.33
median7.5
Q37.75
95-th percentile8.08
Maximum8.75
Range8.75
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.40631592
Coefficient of variation (CV)0.0540493
Kurtosis89.118262
Mean7.5175057
Median Absolute Deviation (MAD)0.17
Skewness-4.8442801
Sum9855.45
Variance0.16509263
MonotonicityNot monotonic
2023-07-28T19:57:04.765998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
7.5 172
13.1%
7.67 145
11.1%
7.58 127
9.7%
7.42 120
9.2%
7.75 103
 
7.9%
7.33 99
 
7.6%
7.83 98
 
7.5%
7.17 71
 
5.4%
7.25 64
 
4.9%
7 46
 
3.5%
Other values (22) 266
20.3%
ValueCountFrequency (%)
0 1
 
0.1%
6.08 1
 
0.1%
6.17 3
 
0.2%
6.33 1
 
0.1%
6.42 1
 
0.1%
6.5 2
 
0.2%
6.58 3
 
0.2%
6.67 4
 
0.3%
6.75 7
 
0.5%
6.83 22
1.7%
ValueCountFrequency (%)
8.75 2
 
0.2%
8.58 7
 
0.5%
8.5 7
 
0.5%
8.42 7
 
0.5%
8.33 7
 
0.5%
8.25 8
 
0.6%
8.17 17
 
1.3%
8.08 16
 
1.2%
8 45
3.4%
7.92 38
2.9%

uniformity
Real number (ℝ)

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8333944
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-28T19:57:04.860004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.67
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55934312
Coefficient of variation (CV)0.056881998
Kurtosis84.152305
Mean9.8333944
Median Absolute Deviation (MAD)0
Skewness-6.9261173
Sum12891.58
Variance0.31286473
MonotonicityNot monotonic
2023-07-28T19:57:04.953895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 1128
86.0%
9.33 112
 
8.5%
8.67 31
 
2.4%
8 25
 
1.9%
6.67 7
 
0.5%
6 3
 
0.2%
7.33 2
 
0.2%
9.5 1
 
0.1%
9 1
 
0.1%
0 1
 
0.1%
ValueCountFrequency (%)
0 1
 
0.1%
6 3
 
0.2%
6.67 7
 
0.5%
7.33 2
 
0.2%
8 25
 
1.9%
8.67 31
 
2.4%
9 1
 
0.1%
9.33 112
 
8.5%
9.5 1
 
0.1%
10 1128
86.0%
ValueCountFrequency (%)
10 1128
86.0%
9.5 1
 
0.1%
9.33 112
 
8.5%
9 1
 
0.1%
8.67 31
 
2.4%
8 25
 
1.9%
7.33 2
 
0.2%
6.67 7
 
0.5%
6 3
 
0.2%
0 1
 
0.1%

clean_cup
Real number (ℝ)

Distinct11
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8331198
Minimum0
Maximum10
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-28T19:57:05.047647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.33
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.77134973
Coefficient of variation (CV)0.07844405
Kurtosis69.173176
Mean9.8331198
Median Absolute Deviation (MAD)0
Skewness-7.3778242
Sum12891.22
Variance0.59498041
MonotonicityNot monotonic
2023-07-28T19:57:05.141391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 1194
91.1%
9.33 58
 
4.4%
8.67 16
 
1.2%
6.67 13
 
1.0%
8 13
 
1.0%
6 6
 
0.5%
5.33 3
 
0.2%
7.33 3
 
0.2%
2.67 2
 
0.2%
0 2
 
0.2%
ValueCountFrequency (%)
0 2
 
0.2%
1.33 1
 
0.1%
2.67 2
 
0.2%
5.33 3
 
0.2%
6 6
 
0.5%
6.67 13
 
1.0%
7.33 3
 
0.2%
8 13
 
1.0%
8.67 16
 
1.2%
9.33 58
4.4%
ValueCountFrequency (%)
10 1194
91.1%
9.33 58
 
4.4%
8.67 16
 
1.2%
8 13
 
1.0%
7.33 3
 
0.2%
6.67 13
 
1.0%
6 6
 
0.5%
5.33 3
 
0.2%
2.67 2
 
0.2%
1.33 1
 
0.1%

sweetness
Real number (ℝ)

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9032723
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-28T19:57:05.235143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.33
Q110
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.53083174
Coefficient of variation (CV)0.05360165
Kurtosis157.52829
Mean9.9032723
Median Absolute Deviation (MAD)0
Skewness-10.756332
Sum12983.19
Variance0.28178234
MonotonicityNot monotonic
2023-07-28T19:57:05.329262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10 1218
92.9%
9.33 61
 
4.7%
8.67 12
 
0.9%
8 8
 
0.6%
6.67 7
 
0.5%
6 3
 
0.2%
1.33 1
 
0.1%
0 1
 
0.1%
ValueCountFrequency (%)
0 1
 
0.1%
1.33 1
 
0.1%
6 3
 
0.2%
6.67 7
 
0.5%
8 8
 
0.6%
8.67 12
 
0.9%
9.33 61
 
4.7%
10 1218
92.9%
ValueCountFrequency (%)
10 1218
92.9%
9.33 61
 
4.7%
8.67 12
 
0.9%
8 8
 
0.6%
6.67 7
 
0.5%
6 3
 
0.2%
1.33 1
 
0.1%
0 1
 
0.1%

cupper_points
Real number (ℝ)

Distinct42
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4978642
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-28T19:57:05.438999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.83
Q17.25
median7.5
Q37.75
95-th percentile8.08
Maximum10
Range10
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.47461002
Coefficient of variation (CV)0.063299362
Kurtosis50.156431
Mean7.4978642
Median Absolute Deviation (MAD)0.25
Skewness-2.8388745
Sum9829.7
Variance0.22525467
MonotonicityNot monotonic
2023-07-28T19:57:05.563996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
7.5 152
11.6%
7.58 136
10.4%
7.33 114
 
8.7%
7.67 113
 
8.6%
7.42 103
 
7.9%
7.25 85
 
6.5%
7.75 84
 
6.4%
7.83 81
 
6.2%
7.17 63
 
4.8%
7.92 52
 
4.0%
Other values (32) 328
25.0%
ValueCountFrequency (%)
0 1
 
0.1%
5.17 1
 
0.1%
5.25 1
 
0.1%
5.42 1
 
0.1%
6 1
 
0.1%
6.17 3
0.2%
6.25 1
 
0.1%
6.33 3
0.2%
6.42 5
0.4%
6.5 6
0.5%
ValueCountFrequency (%)
10 4
0.3%
9.25 1
 
0.1%
9 1
 
0.1%
8.83 1
 
0.1%
8.75 1
 
0.1%
8.67 2
 
0.2%
8.58 5
0.4%
8.5 8
0.6%
8.42 6
0.5%
8.33 8
0.6%

total_cup_points
Real number (ℝ)

Distinct178
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.115927
Minimum0
Maximum90.58
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-28T19:57:05.688997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.92
Q181.17
median82.5
Q383.67
95-th percentile85.5
Maximum90.58
Range90.58
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation3.5157607
Coefficient of variation (CV)0.042814601
Kurtosis229.25664
Mean82.115927
Median Absolute Deviation (MAD)1.25
Skewness-10.529617
Sum107653.98
Variance12.360573
MonotonicityDecreasing
2023-07-28T19:57:05.813998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.17 38
 
2.9%
83 37
 
2.8%
82.42 31
 
2.4%
82.75 29
 
2.2%
82.33 29
 
2.2%
82.67 26
 
2.0%
81.83 26
 
2.0%
82.92 26
 
2.0%
81.67 25
 
1.9%
81.5 24
 
1.8%
Other values (168) 1020
77.8%
ValueCountFrequency (%)
0 1
0.1%
59.83 1
0.1%
63.08 1
0.1%
67.92 1
0.1%
68.33 1
0.1%
69.17 2
0.2%
69.33 1
0.1%
70.67 1
0.1%
70.75 1
0.1%
71 1
0.1%
ValueCountFrequency (%)
90.58 1
0.1%
89.92 1
0.1%
89.75 1
0.1%
89 1
0.1%
88.83 2
0.2%
88.75 1
0.1%
88.67 1
0.1%
88.42 1
0.1%
88.25 1
0.1%
88.08 1
0.1%

altitude_mean_meters
Real number (ℝ)

MISSING  SKEWED 

Distinct201
Distinct (%)18.5%
Missing227
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1784.1964
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.5 KiB
2023-07-28T19:57:05.954626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile442
Q11100
median1310.64
Q31600
95-th percentile1880
Maximum190164
Range190163
Interquartile range (IQR)500

Descriptive statistics

Standard deviation8767.0169
Coefficient of variation (CV)4.9137063
Kurtosis414.4035
Mean1784.1964
Median Absolute Deviation (MAD)239.36
Skewness20.095181
Sum1934068.9
Variance76860586
MonotonicityNot monotonic
2023-07-28T19:57:06.079626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 66
 
5.0%
1400 52
 
4.0%
1100 52
 
4.0%
1300 50
 
3.8%
1500 44
 
3.4%
1250 39
 
3.0%
1700 36
 
2.7%
1600 35
 
2.7%
1750 34
 
2.6%
1550 34
 
2.6%
Other values (191) 642
49.0%
(Missing) 227
 
17.3%
ValueCountFrequency (%)
1 12
0.9%
12 3
 
0.2%
13 2
 
0.2%
50 1
 
0.1%
100 1
 
0.1%
110 1
 
0.1%
125 1
 
0.1%
150 2
 
0.2%
157.8864 3
 
0.2%
165 1
 
0.1%
ValueCountFrequency (%)
190164 2
0.2%
110000 1
0.1%
11000 1
0.1%
4287 1
0.1%
4001 1
0.1%
3850 1
0.1%
3845 1
0.1%
3825 1
0.1%
3800 1
0.1%
3500 1
0.1%

Interactions

2023-07-28T19:57:00.394039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:38.385345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:40.857132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:43.347004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:48.607055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:50.323957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:51.672608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:53.134796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:54.599213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:55.995541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:57.531514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:59.014482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:00.531418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:38.694609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:41.039646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:43.539581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:48.991525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:50.422304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:51.788331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:53.252414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:54.705255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:56.095402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:57.632559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:59.132203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:00.650097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:38.876997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:41.229607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:43.725680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:49.139824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:50.538293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:51.887286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:53.360905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:54.829657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:56.212697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:57.748766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:59.282402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:00.768482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:39.060205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:41.406768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:44.035740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:49.256276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:50.639738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:51.987671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:53.468843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:54.917853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:56.312934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:57.865538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:59.380992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:00.891051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:39.245521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:41.666718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:44.227777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:49.380629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:50.757141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:52.103647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:53.635709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:55.034667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:56.434997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:57.981663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:59.481662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:00.998231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:39.455369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:41.870426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:44.397015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:49.506069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:50.855043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:52.310611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:53.786101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:55.162695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:56.540967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:58.097703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:59.601026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:01.127895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:39.639513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:42.075059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:44.735662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:49.607305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:50.971978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:52.437913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:53.886981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:55.266810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:56.650317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:58.226846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:59.713557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:01.267451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:39.831708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:42.280120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:44.962384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:49.727603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:51.089279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:52.551522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:53.991634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:55.367723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:56.801482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:58.337998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:59.831694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:01.430013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:40.039974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:42.485560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:45.279380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:49.841685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:51.187868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:52.653051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:54.117786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:55.484186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:56.898935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:58.550209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:59.930811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:01.575054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:40.220392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:42.686972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:46.135543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:49.957011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:51.303769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:52.773144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:54.218821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:55.606935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:57.017087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:58.664805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:00.064058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:01.726877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:40.447148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:42.896906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:47.077972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:50.069992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:51.429507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:52.890219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:54.344608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:55.748044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:57.296609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:58.782578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:00.163333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:01.860123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:40.636939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:43.106674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:48.312345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:50.188799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:51.545531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:53.019262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:54.463302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:55.858275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:57.403979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:56:58.879869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T19:57:00.281224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-07-28T19:57:06.204626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
aromaflavoraftertasteaciditybodybalanceuniformityclean_cupsweetnesscupper_pointstotal_cup_pointsaltitude_mean_meterscountry_of_originvarietyprocessing_method
aroma1.0000.7150.6670.6160.5610.6160.1160.1610.0240.6450.7520.2280.1520.0670.016
flavor0.7151.0000.8060.7440.6690.7260.1600.1990.0500.7970.8630.1860.2040.0280.063
aftertaste0.6670.8061.0000.6970.6760.7570.1580.1770.0200.7800.8400.2000.2690.0970.074
acidity0.6160.7440.6971.0000.6160.6600.1050.1090.0130.6880.7690.2430.2130.0000.000
body0.5610.6690.6760.6161.0000.6970.0340.073-0.0700.6700.7260.1750.1700.0680.073
balance0.6160.7260.7570.6600.6971.0000.1250.144-0.0230.7560.8100.2360.2140.0490.129
uniformity0.1160.1600.1580.1050.0340.1251.0000.6210.4680.1440.3450.0680.0450.0000.032
clean_cup0.1610.1990.1770.1090.0730.1440.6211.0000.4870.1790.3640.0820.0210.0000.000
sweetness0.0240.0500.0200.013-0.070-0.0230.4680.4871.0000.0370.1910.0140.1570.0000.069
cupper_points0.6450.7970.7800.6880.6700.7560.1440.1790.0371.0000.8560.2170.2030.0550.069
total_cup_points0.7520.8630.8400.7690.7260.8100.3450.3640.1910.8561.0000.2520.1890.0000.068
altitude_mean_meters0.2280.1860.2000.2430.1750.2360.0680.0820.0140.2170.2521.0000.0380.0000.000
country_of_origin0.1520.2040.2690.2130.1700.2140.0450.0210.1570.2030.1890.0381.0000.4770.397
variety0.0670.0280.0970.0000.0680.0490.0000.0000.0000.0550.0000.0000.4771.0000.284
processing_method0.0160.0630.0740.0000.0730.1290.0320.0000.0690.0690.0680.0000.3970.2841.000

Missing values

2023-07-28T19:57:02.063737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-28T19:57:02.324279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-28T19:57:02.538023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ownercountry_of_originvarietyprocessing_methodaromaflavoraftertasteaciditybodybalanceuniformityclean_cupsweetnesscupper_pointstotal_cup_pointsaltitude_mean_meters
1metad plcEthiopiaNaNWashed / Wet8.678.838.678.758.508.4210.0010.010.008.7590.582075.0
2metad plcEthiopiaOtherWashed / Wet8.758.678.508.588.428.4210.0010.010.008.5889.922075.0
3grounds for health adminGuatemalaBourbonNaN8.428.508.428.428.338.4210.0010.010.009.2589.751700.0
4yidnekachew dabessaEthiopiaNaNNatural / Dry8.178.588.428.428.508.2510.0010.010.008.6789.002000.0
5metad plcEthiopiaOtherWashed / Wet8.258.508.258.508.428.3310.0010.010.008.5888.832075.0
6ji-ae ahnBrazilNaNNatural / Dry8.588.428.428.508.258.3310.0010.010.008.3388.83NaN
7hugo valdiviaPeruOtherWashed / Wet8.428.508.338.508.258.2510.0010.010.008.5088.75NaN
8ethiopia commodity exchangeEthiopiaNaNNaN8.258.338.508.428.338.5010.0010.09.339.0088.671635.0
9ethiopia commodity exchangeEthiopiaNaNNaN8.678.678.588.428.338.429.3310.09.338.6788.421635.0
10diamond enterprise plcEthiopiaOtherNatural / Dry8.088.588.508.507.678.4210.0010.010.008.5088.251822.5
ownercountry_of_originvarietyprocessing_methodaromaflavoraftertasteaciditybodybalanceuniformityclean_cupsweetnesscupper_pointstotal_cup_pointsaltitude_mean_meters
1302kurt kappeliMexicoTypicaWashed / Wet6.927.006.836.927.426.926.006.0010.006.7570.751000.00
1303volcafe ltda. - brasilBrazilNaNNatural / Dry7.007.006.837.007.336.836.006.0010.006.6770.67NaN
1304cadexsaHondurasCatuaiWashed / Wet6.676.506.176.676.836.178.008.008.006.3369.331450.00
1305cadexsaHondurasCatuaiWashed / Wet7.006.176.176.676.506.178.008.008.006.5069.171450.00
1306cadexsaHondurasCatuaiWashed / Wet7.006.336.176.506.676.178.008.008.006.3369.171450.00
1307juan carlos garcia lopezMexicoBourbonWashed / Wet7.086.836.257.427.256.7510.000.0010.006.7568.33900.00
1308myriam kaplan-pasternakHaitiTypicaNatural / Dry6.756.586.426.677.086.679.336.006.006.4267.92350.00
1309exportadora atlantic, s.a.NicaraguaCaturraOther7.256.586.336.256.426.086.006.006.006.1763.081100.00
1310juan luis alvarado romeroGuatemalaCatuaiWashed / Wet7.506.676.677.677.336.678.001.331.336.6759.831417.32
1312bismarck castroHondurasCaturraNaN0.000.000.000.000.000.000.000.000.000.000.001400.00